References, the mechanism scientists rely on to signal previous knowledge,
lately have turned into widely used and misused measures of scientific impact.
Yet, when a discovery becomes common knowledge, citations suffer from
obliteration by incorporation. This leads to the concept of hidden citation,
representing a clear textual credit to a discovery without a reference to the
publication embodying it. Here, we rely on unsupervised interpretable machine
learning applied to the full text of each paper to systematically identify
hidden citations. We find that for influential discoveries hidden citations
outnumber citation counts, emerging regardless of publishing venue and
discipline. We show that the prevalence of hidden citations is not driven by
citation counts, but rather by the degree of the discourse on the topic within
the text of the manuscripts, indicating that the more discussed is a discovery,
the less visible it is to standard bibliometric analysis. Hidden citations
indicate that bibliometric measures offer a limited perspective on quantifying
the true impact of a discovery, raising the need to extract knowledge from the
full text of the scientific corpus.

通过对每篇论文的全文应用无监督可解释的机器学习方法，系统地识别出隐藏的引文，发现对有影响力的发现来说，隐藏的引文数量超过引文计数，不受出版渠道和学科的影响。隐藏的引文数量取决于论文内对该主题讨论的程度，表明对于标准的文献计量分析而言，讨论得越多的发现越不可见。隐藏的引文表明文献计量衡量了一个发现的影响的真实度有限，因此需要从科学文献的全文中提取知识。